Systems and methods for determining contextually-relevant keywords

ABSTRACT

Disclosed is a system for determining contextually-relevant keywords related to text of an electronic document. The system comprises a memory, a database arrangement comprising an ontology and a synonym databank, a data processing arrangement and a sever arrangement. The data processing arrangement is operable to fetch the electronic document stored within the memory. Furthermore, the data processing arrangement determines the common words from the ontology and the synonyms corresponding to the common words from synonym databank. Moreover, the data processing arrangement determines the generated set of keywords comprising the common words and the synonyms. The server arrangement determines a preference score and an importance score for each keyword of the generated set of keywords. Furthermore, the server arrangement determines the cumulative rank of each keyword based on the preference score and the importance score, therefore determining contextually-relevant keywords.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a non-provisional patent application based upon a U.S.provisional patent application No. 62/664,415 as filed on Apr. 30, 2018,and claims priority under 35 U.S.C. 199(e).

TECHNICAL FIELD

The present disclosure relates generally to data processing; and morespecifically, to systems and methods for determiningcontextually-relevant keywords. Furthermore, the present disclosurerelates to software products recorded on machine-readable non-transientdata storage media, wherein the software products are executable uponcomputing hardware to implement the aforesaid methods of determiningcontextually-relevant keywords.

BACKGROUND

The internet comprises a large amount of information in a form ofelectronic documents, images, presentations and so forth. Suchinformation is frequently exchanged between users. Furthermore, with anincrease in a rate of exchange of such information between users, thereexists a need for exchanging only selective information. For instance,prior to sharing a research document as a whole, a researcher associatedwith the research document will share a crux of the research document ina form of abstract, summary, keywords, metadata, literature review andso forth, that provides selective information about the researchdocument to a prospective buyer (or a licensee). It will be appreciatedthat providing such selective information about the research document tothe prospective buyer ensures that significant findings published withinthe research document is not revealed to the prospective buyer prior topurchase of the research document by the prospective buyer, while alsoguaranteeing that relevant information required by the prospective buyerto make a decision to purchase the research document is providedthereto. For example, a researcher associated with a research documentmay generate or extract one or more significant keywords associated withtext of the research document. In such an example, the keywords for theresearch document will serve as points for information published withinthe research document without revealing significant findings therein,thereby enabling the research document to reach targeted usersefficiently.

However, despite the keywords being an important facet betweenelectronic documents and accessibility thereof to users, most electronicdocuments do not have keywords assigned thereto. On the other hand, if aparticular electronic document does have keywords assigned thereto,conventionally, the keywords are assigned by an author of the electronicdocument based completely on a judgment thereof. It will be appreciatedthat such a conventional approach of assignment of the keywords toelectronic documents is associated with various problems. Firstly, thekeywords selected by the author may not be highly relevant to a text ofthe particular electronic document. Another problem associated with theconventional approach is that technical knowledge and/or technicalvocabulary of the author of the electronic document may have a limitedcapacity, thereby, limiting a quality of the keywords that are selectedby the author for the electronic document. Moreover, the author of theparticular electronic document may be unaware of popular keywords thatare used by end-users to search for content associated with theelectronic document, thus, limiting accessibility (or visibility) of theelectronic document to the end-users. In such an instance, determinationand subsequent assignment of such popular keywords by the author will bea time consuming and tedious process for the author.

Therefore, in light of the foregoing discussion, there exists a need toovercome the aforementioned problems associated with the conventionaltechniques of determination and/or assignment of relevant keywords forelectronic documents.

SUMMARY

The present disclosure seeks to provide a system of determiningcontextually-relevant keywords related to text of an electronicdocument. The present disclosure also seeks to provide a method ofdetermining contextually-relevant keywords related to text of anelectronic document. Furthermore, the present disclosure seeks toprovide a software product recorded on machine-readable non-transientdata storage media, wherein the software product is executable uponcomputing hardware to implement the aforesaid method of determiningcontextually-relevant keywords related to text of an electronicdocument.

The present disclosure seeks to provide a solution to the existingproblems associated with determination of keywords that are contextuallyrelevant to electronic documents. An aim of the present disclosure is toprovide a solution that overcomes at least partially the problemsencountered in prior art, and provide the system and method fordetermination of contextually-relevant keywords for electronicdocuments, thereby enabling to enhance an accessibility (or visibility)of the electronic documents to end-users while safeguarding criticalinformation published within the electronic documents, as well asreducing time and effort required to be exerted by authors of theelectronic documents in determining such contextually-relevant keywords.

In one aspect, an embodiment of the present disclosure provides a systemfor determining contextually-relevant keywords related to text of anelectronic document, the system comprising:

-   a memory operable to store the electronic document therein;-   a database arrangement comprising an ontology and a synonym    databank;-   a data processing arrangement communicatively coupled to the memory    and the database arrangement, wherein the data processing    arrangement is operable to    -   fetch the electronic document stored within the memory,    -   extract the text of the electronic document,    -   receive a plurality of words from the ontology,    -   compare each of the plurality of words with the text of the        electronic document, to identify common words from the ontology        that appear within the electronic document,    -   receive from the synonym databank, a plurality of synonymous        words corresponding to the common words, and    -   generate a set of keywords comprising the common words and the        plurality of synonymous words corresponding to the common words;        and-   a server arrangement communicatively coupled to the database    arrangement and the data processing arrangement, wherein the server    arrangement is operable to    -   receive the set of keywords from the data processing        arrangement,    -   determine a preference score for each keyword of the set of        keywords, wherein the preference score is based on one or more        preference parameters,    -   identify a semantic relationship of each keyword with other        keywords of the set of keywords,    -   determine an importance score of each keyword of the set of        keywords, based on the semantic relationship,    -   determine a cumulative rank for each keyword of the set of        keywords, based on the preference score and the importance score        of the keyword, and    -   determine, based on the cumulative rank of each keyword of the        set of keywords, the contextually-relevant keywords related to        the text of electronic document.

In another aspect, an embodiment of the present disclosure provides amethod for determining contextually-relevant keywords related to text ofan electronic document, wherein the method comprises:

-   extracting the text of the electronic document;-   receiving a plurality of words from an ontology;-   comparing each of the plurality of words with the text of the    electronic document, to identify common words from the ontology that    appear within the electronic document;-   receiving a plurality of synonymous words corresponding to the    common words;-   generating a set of keywords comprising the common words and the    plurality of synonymous words corresponding to the common words;-   determining a preference score for each keyword of the set of    keywords, wherein the preference score is based on one or more    preference parameters;-   identifying a semantic relationship of each keyword with other    keywords of the set of keywords;-   determining an importance score of each keyword of the set of    keywords, based on the semantic relationship;-   determining a cumulative rank for each keyword of the set of    keywords, based on the preference score and the importance score of    the keyword; and-   determining, based on the cumulative rank of each keyword of the set    of keywords, the contextually-relevant keywords related to the text    of electronic document.

In yet another aspect, an embodiment of the present disclosure providesa software product recorded on machine-readable non-transient datastorage media, characterized in that the software product is executableupon computing hardware to implement the aforementioned method fordetermining contextually-relevant keywords related to text of anelectronic document.

Embodiments of the present disclosure substantially eliminate or atleast partially address the aforementioned problems in the prior art,and enables selection of contextually-relevant keywords for electronicdocuments, thereby, reducing time and effort required for such selectionof the contextually-relevant keywords by authors of the electronicdocuments. Furthermore, such contextually-relevant keywords enhanceaccessibility (or visibility) of the electronic documents for targetaudiences.

Additional aspects, advantages, features and objects of the presentdisclosure would be made apparent from the drawings and the detaileddescription of the illustrative embodiments construed in conjunctionwith the appended claims that follow.

It will be appreciated that features of the present disclosure aresusceptible to being combined in various combinations without departingfrom the scope of the present disclosure as defined by the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The summary above, as well as the following detailed description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating the presentdisclosure, exemplary constructions of the disclosure are shown in thedrawings. However, the present disclosure is not limited to specificmethods and instrumentalities disclosed herein. Moreover, those skilledin the art will understand that the drawings are not to scale. Whereverpossible, like elements have been indicated by identical numbers.

Embodiments of the present disclosure will now be described, by way ofexample only, with reference to the following diagrams wherein:

FIG. 1 is a schematic illustration of a system for determiningcontextually-relevant keywords, in accordance with an embodiment of thepresent disclosure; and

FIGS. 2A and 2B are illustrations of steps of a method for determiningcontextually-relevant keywords, in accordance with an embodiment of thepresent disclosure.

In the accompanying drawings, an underlined number is employed torepresent an item over which the underlined number is positioned or anitem to which the underlined number is adjacent. A non-underlined numberrelates to an item identified by a line linking the non-underlinednumber to the item. When a number is non-underlined and accompanied byan associated arrow, the non-underlined number is used to identify ageneral item at which the arrow is pointing.

DETAILED DESCRIPTION OF EMBODIMENTS

The following detailed description illustrates embodiments of thepresent disclosure and ways in which they can be implemented. Althoughsome modes of carrying out the present disclosure have been disclosed,those skilled in the art would recognize that other embodiments forcarrying out or practising the present disclosure are also possible.

In one aspect, an embodiment of the present disclosure provides a systemfor determining contextually-relevant keywords related to text of anelectronic document, the system comprising:

-   a memory operable to store the electronic document therein;-   a database arrangement comprising an ontology and a synonym    databank;-   a data processing arrangement communicatively coupled to the memory    and the database arrangement, wherein the data processing    arrangement is operable to    -   fetch the electronic document stored within the memory,    -   extract the text of the electronic document,    -   receive a plurality of words from the ontology,    -   compare each of the plurality of words with the text of the        electronic document, to identify common words from the ontology        that appear within the electronic document,    -   receive from the synonym databank, a plurality of synonymous        words corresponding to the common words, and    -   generate a set of keywords comprising the common words and the        plurality of synonymous words corresponding to the common words;        and-   a server arrangement communicatively coupled to the database    arrangement and the data processing arrangement, wherein the server    arrangement is operable to    -   receive the set of keywords from the data processing        arrangement,    -   determine a preference score for each keyword of the set of        keywords, wherein the preference score is based on one or more        preference parameters,    -   identify a semantic relationship of each keyword with other        keywords of the set of keywords,    -   determine an importance score of each keyword of the set of        keywords, based on the semantic relationship,    -   determine a cumulative rank for each keyword of the set of        keywords, based on the preference score and the importance score        of the keyword, and    -   determine, based on the cumulative rank of each keyword of the        set of keywords, the contextually-relevant keywords related to        the text of electronic document.

In another aspect, an embodiment of the present disclosure provides amethod for determining contextually-relevant keywords related to text ofan electronic document, wherein the method comprises:

-   extracting the text of the electronic document;-   receiving a plurality of words from an ontology;-   comparing each of the plurality of words with the text of the    electronic document, to identify common words from the ontology that    appear within the electronic document;-   receiving a plurality of synonymous words corresponding to the    common words;-   generating a set of keywords comprising the common words and the    plurality of synonymous words corresponding to the common words;-   determining a preference score for each keyword of the set of    keywords, wherein the preference score is based on one or more    preference parameters;-   identifying a semantic relationship of each keyword with other    keywords of the set of keywords;-   determining an importance score of each keyword of the set of    keywords, based on the semantic relationship;-   determining a cumulative rank for each keyword of the set of    keywords, based on the preference score and the importance score of    the keyword; and-   determining, based on the cumulative rank of each keyword of the set    of keywords, the contextually-relevant keywords related to the text    of electronic document.

In yet another aspect, an embodiment of the present disclosure providesa software product recorded on machine-readable non-transient datastorage media, characterized in that the software product is executableupon computing hardware to implement the aforementioned method fordetermining contextually-relevant keywords related to text of anelectronic document.

The present disclosure provides the system and method for determinationof contextually-relevant keywords for electronic documents. Theelectronic documents, such as research documents, are meant to reach atarget audience, such as a plurality of users that want to access theresearch documents. However, prior to obtaining legitimate access to theelectronic documents, significant findings therein are required to beconcealed from such plurality of users. The system and method allowdetermination of the contextually-relevant keywords for the electronicdocuments that improves the accessibility of the electronic document forthe targeted audience. Moreover, the determination of suchcontextually-relevant keywords enables a time efficient process for theauthor of the electronic document. Furthermore, such a system and methodreduce the laborious effort required for the selection of keywords bythe author of the electronic document.

The system comprises a memory operable to store the electronic documenttherein. The electronic document is stored within the memory, whereinthe memory can be implemented as a solid-state drive memory, a hard diskdrive memory, double data rate random access memory and so forth.

In an embodiment, the electronic document is stored within the memory inan encrypted format. The encryption of the electronic document enablesto store the electronic document securely within the memory. In anexample, the electronic document is a research document. Furthermore, anauthor of the research document would prefer to store the researchdocument within a personal computing device thereof, as the author wouldnot want a content of the research document to be revealed to athird-party without his consent. In such an example, the researchdocument will be stored in a memory of the personal computing device ofthe author in the encrypted format, so that the third-party cannotaccess the content of the research document without obtaining theconsent from the author.

The system comprises a database arrangement comprising an ontology and asynonym databank. The term “database arrangement” as used throughout thepresent disclosure, relates to programmable and/or non-programmablecomponents that allow storage of data therein, such as, data in a formof text, numbers, text fields, numerical fields, selections, tables,columns and so forth. The term “ontology” as used throughout the presentdisclosure, relates to a collection of data in a form of text comprisingwords, key-phrases, concepts and so forth. For example, the ontologycorresponds to “life sciences” and the data is associated with theontology relates to cell division, cell mutation, blood cancer and soforth. The term “synonym databank” as used throughout the presentdisclosure, relates to a collection of synonymous words corresponding tothe words of the ontology. It will be appreciated that the collection ofdata in the ontology and will be interlinked with the collection ofsynonymous words within the synonym databank. For example, the ontologycomprises a word “cancer”. In such an example, the synonym databank willhave the synonymous words corresponding to the word “cancer” such as“tumour”, “malignance”, “carcinoma” and so forth stored therein.

In an embodiment, the ontology is a hierarchical ontology comprising aplurality of nodes, and wherein each of the plurality of nodes isassociated with a plurality of words related to a specificsubject-matter. The hierarchical ontology comprises the plurality ofnodes, wherein each node of the plurality of nodes can be further linkedto one or more nodes. Furthermore, each node of the plurality of nodesis associated with a plurality of specific words of the ontology.Furthermore, such a plurality of specific words relate to a specificsubject-matter, such as a subset of the subject-matter that the ontologycorresponds thereto. In an example, the ontology relates to“life-sciences” and comprises three nodes related to subject-matters of“diseases”, “causes of the diseases” and “drugs”. Furthermore, the node“diseases” is further linked to nodes “Typhoid”, “Malaria”, “Dengue” andso forth. Moreover, the node “diseases” is associated to a plurality ofspecific words such as “communicable diseases”, “infections”, “fever”and so forth. The node “causes of the diseases” is further linked tonodes “Bacteria”, “Virus”, “Protozoa”, “Fungi” and so forth. The node“drugs” is further linked to nodes “Chloroquine”, “Mefloquine”, “Advil”,“Tylenol” and so forth.

Furthermore, the system comprises a data processing arrangementcommunicatively coupled to the memory and the database arrangement,wherein the data processing arrangement is operable to fetch theelectronic document stored within the memory. The data processingarrangement is communicatively coupled to the memory and the databasearrangement, such as, via a communication network. In an example, thecommunication network includes, but is not limited to, a short rangeradio communication network (for example, Bluetooth®, Bluetooth LowEnergy (BLE), Infrared, ZigBee® and so forth), Local Area Networks(LANs), Wide Area Networks (WANs), Metropolitan Area Networks (MANs),Wireless LANs (WLANs), Wireless WANs (WWANs), Wireless MANs (WMANs), theInternet, cellular networks (for example, second generation (2G), thirdgeneration (3G), fourth generation (4G), fifth generation (5G) or sixthgeneration (6G) telecommunication networks), Worldwide Interoperabilityfor Microwave Access (WiMAX) networks or any combination thereof.Moreover, the data processing arrangement fetches the electronicdocument from the memory. The electronic document stored in the memorycan be in a form of a word-processor document format (such as .doc,.docx and so forth), image format (such as .png, .jpg, .jpeg and soforth), presentation file-format (such as .ppt, .pptx, .pot and soforth), portable document format (such as .pdf and so forth),spreadsheet file format (such as .xlx, .xls and so forth), and the like.Furthermore, the data processing arrangement is operable to extract thetext of the electronic document. The data processing arrangement fetchesthe electronic document from the memory and subsequently, extracts thetext within the electronic document. It will be appreciated that theelectronic document can comprise textual data, pictorial representationof data, mathematical equations and so forth. In such an instance, thedata processing arrangement extracts the textual data (referred to as“text” throughout the present disclosure) from the electronic document.Optionally, the data processing arrangement extracts the text of theelectronic document in JavaScript Object Notation (.json) file format.In an example, a set of experimental data recorded within a researchdocument in a word-processor document (such as .doc) is stored in amemory of a computing device of a researcher associated with theresearch document. The research document comprises textual data, graphs,schematic diagrams, block diagrams and chemical equations. In such anexample, the data processing arrangement fetches the research documentstored in the word-processor document format, from the memory of thecomputing device of the researcher. Subsequently, the data processingarrangement extracts the textual data from the research document inJavaScript Object Notation (.json) file format.

Furthermore, the data processing arrangement is operable to receive aplurality of words from the ontology. As mentioned hereinbefore, theontology comprises a plurality of nodes corresponding to the specificsubject-matter, wherein each of the plurality of nodes is associatedwith a plurality of words. The data processing arrangement is operableto receive all such plurality of words corresponding to each of theplurality of nodes associated with the ontology. Optionally, theplurality of words is received in succession by the data processingarrangement, such as, based on a node that a word of the plurality ofwords corresponds thereto. Alternatively, the data processingarrangement is operable to receive all words at a same time.

In an embodiment, the data processing arrangement is further operable toreceive an input from a user associated with the electronic document,wherein the received input corresponds to a subject-matter associatedwith the text of the electronic document. The user associated with theelectronic document can provide an input corresponding to thesubject-matter (such as a technical field) of the electronic document.The data processing arrangement will receive the input provided by theuser. In an example, the user is a researcher who has performedexperiments related to a subject-matter of lung cancer. The researcherwill provide the input corresponding to the subject-matter as “lungcancer” and the data processing arrangement will receive such an inputprovided by the researcher.

Furthermore, the data processing arrangement is operable to compare thesubject-matter corresponding to the received input with thesubject-matter corresponding to the plurality of nodes. The inputreceived from the user is compared with the plurality of nodes of theontology by the data processing arrangement, wherein each node of theplurality of nodes relates to the specific subject-matter. Furthermore,the data processing arrangement is operable to identify, based on thecomparison, the node of the plurality of nodes related to thesubject-matter corresponding to the received input. The data processingarrangement identifies the node of the plurality of nodes of theontology that is identical to the received input from the user.Furthermore, the data processing arrangement is operable to receive theplurality of words associated with the identified node of the pluralityof nodes. The identified plurality of words corresponding to the nodeidentical to the received input from the user, is received by the dataprocessing arrangement. In an example, the input received from the useris “lung cancer” and the received input is compared with the pluralityof nodes of the ontology. The node corresponding to “Lung cancer” isidentified by the data processing arrangement. It will be appreciatedthat the received input “Lung cancer” is identical to the subject-matterof the identified node corresponding to the “Lung cancer”. Consequently,plurality of words associated with the identified node of “Lung cancer”is received by the data processing arrangement.

The data processing arrangement is operable to compare each of theplurality of words with the text of the electronic document, to identifycommon words from the ontology that appear within the electronicdocument. The plurality of words received from the ontology is comparedwith the text extracted from the electronic document. Moreover, commonwords that appear in the ontology and the text of the electronicdocument are identified. In an example, a research document compriseswords “the”, “top”, “drugs”, “for” and “cancer”. It will be appreciatedthat the ontology may only comprise words related to a specificsubject-matter and therefore, words such as “the”, “top”, “for” and soforth may not be present therein. The data processing arrangement willfetch words from the ontology, compare the words with the extracted textof the research document and identify the common words as “drugs” and“cancer”. Optionally, an n-gram model is used for the comparison of theplurality of words received from the ontology and the text extractedfrom the electronic document. It will be appreciated that the n-grammodel relates to a contiguous sequence of ‘n’ items from a givenplurality of words (such as a sentence), wherein ‘n’ represents a numberof words within each sentence. Furthermore, a sentence having one wordis referred as unigram or one-gram, a sentence having two words arereferred as bigram or two-gram, a sentence having three words arereferred as trigram or three-gram and so forth. In an example, theplurality of words associated with the text extracted from theelectronic document may be “top drugs for cancer”, “top drugs for”,“drugs for cancer”, “top drugs”, “drugs for”, “for cancer”, “top”,“drugs”, “for” and “cancer”. In such an example, the plurality of words“top drugs for cancer” is a four-gram. Similarly, the plurality of words“top drugs for”, and “drugs for cancer” are trigrams or three-grams, theplurality of words “top drugs”, “drugs for”, and “for cancer” arebigrams or two-gram and the plurality of words “top”, “drugs”, “for” and“cancer” are unigrams or one-grams.

Furthermore, the data processing arrangement is operable to receive fromthe synonym databank, a plurality of synonymous words corresponding tothe common words. The common words identified from the ontology and thetext extracted from electronic document, are looked up in the synonymdatabank by the data processing arrangement. Moreover, the synonymouswords corresponding to the identified common words are received by thedata processing arrangement from the synonym databank. In an example, aplurality of words of the ontology is compared with a research documentand common words are identified to be “cancer”, “blood”, “skin” and soforth. The synonymous words corresponding to the word “cancer”,identified as “tumour”, “malignance”, “carcinoma” and so forth, arereceived from the synonym databank. Moreover, the synonymous wordscorresponding to the word “blood”, identified as “plasma”, “body fluid”and so forth, are received from the synonym databank. Moreover, thesynonymous words corresponding to the word “skin”, identified as“membrane”, “epidermis”, “dermis” and so forth, are received from thesynonym databank.

In an embodiment, the data processing arrangement is operable toidentify the common words and receive the plurality of synonymous wordscorresponding to the common words in a sequential manner. The dataprocessing arrangement identifies the common words between the pluralityof words received from the ontology and the text extracted from theelectronic document. Subsequently, the data processing arrangementreceives the synonymous words from the synonym databank corresponding tothe common words of the ontology. In an example, the data processingarrangement identifies the common words as “skin”, “tumour” and“disease”. Subsequently, the data processing arrangement looks up forsynonyms of the words “skin”, “tumour” and “disease” from the synonymdatabank. Consequently, the data processing arrangement receives thesynonyms of the word “skin” as “membrane”, “epidermis” and “dermis”, thesynonyms of the word “tumour” as “malignance”, “carcinoma” and “cancer”and the synonyms of the word “disease” as “illness”, “sickness” and “illhealth” from the synonym databank. In another embodiment, the dataprocessing arrangement is operable to identify the common words andreceive the plurality of synonymous words corresponding to the commonwords in a simultaneous manner. The data processing arrangementidentifies a common word between plurality of words received from theontology and the text extracted from the electronic document.Simultaneously, the data processing arrangement receives the synonyms ofthe common word from the synonym databank. Subsequently, the dataprocessing arrangement identifies a next common word between theplurality of words received from ontology and the extracted text.Thereafter, the data processing arrangement receives the synonyms of thenext common word from the synonym databank. In an example, the dataprocessing arrangement identifies a common word from a research documentas “skin”. Simultaneously, the data processing arrangement looks up forsynonyms of the word “skin” in the synonym databank and receives thesynonyms corresponding to the word “skin” as “membrane”, “epidermis” and“dermis”. Subsequently, the data processing arrangement identifies anext common word from the research document as “tumour”. Simultaneously,the data processing arrangement looks up for synonyms of the word“tumour” in the synonym databank and receives the synonyms correspondingto the word “tumour” as “malignance”, “carcinoma” and “cancer”.

Furthermore, the data processing arrangement is operable to generate aset of keywords comprising the common words and the plurality ofsynonymous words corresponding to the common words. The data processingarrangement combines the common words identified using the ontology andthe synonymous words corresponding to the received common words(received from the synonym databank) and generates the set comprisingthe combination of the words. In an example, the common words identifiedbetween a research document and an ontology are “skin”, “tumour” and“disease”. The corresponding synonyms of the word “skin” are “membrane”,“epidermis” and “dermis”, the corresponding synonyms of the word“tumour” are “malignance”, “carcinoma” and “cancer” and thecorresponding synonyms of the word “disease” are “illness”, “sickness”and “ill health”. The data processing arrangement will therefore,generate the set of keywords comprising the words “skin”, “tumour”,“disease”, “membrane”, “epidermis”, “dermis”, “malignance”, “carcinoma”,“cancer”, “illness”, “sickness” and “ill health”, wherein the generatedset of keywords does not comprise the keywords in any particular order.

Furthermore, the system comprises a server arrangement communicativelycoupled to the data processing arrangement and the database arrangement,wherein the server arrangement is operable to receive the generated setof keywords from the data processing arrangement. The term “serverarrangement” as used throughout the present disclosure, relates to anarrangement of programmable and/or non-programmable components. Theserver arrangement can be implemented in a centralized architecture, adecentralized architecture or a distributed architecture of theprogrammable and/or non-programmable components. In an example, theserver arrangement is implemented using cloud servers. The set ofkeywords generated by the data processing arrangement is received by theserver arrangement, wherein the server arrangement is communicativelycoupled to the data processing arrangement via a communication network(as mentioned hereinbefore).

Furthermore, the server arrangement is operable to determine apreference score for each keyword of the generated set of keywords,wherein the preference score is based on one or more preferenceparameters. As mentioned hereinabove, the generated set of keywords doesnot comprise the keywords in any particular order. In such an instance,the server arrangement assigns the preference score to each keyword ofthe generated set of keywords to determine an order (such as an order ofrelevance) of each keyword within the generated set of keywords. Thepreference score of the generated set of keywords is determined by theserver arrangement based on the one or more preference parameters.

In an embodiment, the one or more preference parameters comprise:frequency of occurrence of each keyword of the set of keywords withinsearch strings previously used by a plurality of users of a searchengine, frequency of occurrence of the common words within the text ofthe electronic document, location of occurrence of the common wordswithin the text of the electronic document and publicly-availableinformation corresponding to each keyword of the generated set ofkeywords of the electronic document. The preference score of a keyworddepends on the frequency of occurrence of the keyword within searchstrings previously used by a plurality of users of a search engine usedto search for information corresponding to the keyword. In such aninstance, a keyword of the generated set of keywords that occurs morefrequently in the search strings is given higher preference score ascompared to a keyword of the generated set of keywords that occurs lessfrequently in the search strings. In an example, a research documentcomprises a word “carcinoma”. The synonyms associated with the word“carcinoma” such as “cancer”, “tumor” and so forth will be received fromthe synonym databank. It will be appreciated that the word “cancer” is awell-known term as compared to the word “carcinoma”. Therefore, aplurality of users searching for the word “cancer” will be higher, ascompared to a plurality of users searching for the word “carcinoma”.Consequently, the frequency of occurrence of the word “cancer” is morethan the frequency of occurrence of the word “carcinoma” within searchstrings. Therefore, the word “cancer” is given the higher preferencescore as compared to the preference score given to the word “carcinoma”.Furthermore, the preference score depends on the frequency of occurrenceof the common words within the text of the electronic document. In suchan instance, a word that occurs more frequently within the text of theelectronic document is given higher preference score as compared to wordthat occurs less frequently within the text of the electronic document.In an example, a research document comprises a word “leukaemia” thatoccurs ten times in the research document, a word “blood” that occurseight times in the research document and a word “infection” that occursfour times in the research document. The frequency of occurrence of theword “leukaemia” is more than the frequency of occurrence of the word“blood” and the frequency of occurrence of the word “blood” is more thanthe word “infection”. Therefore, the word “leukaemia” is assigned ahigher preference score than the word “blood” and the word “blood” isassigned a higher preference score than the word “infection”. Moreover,the preference score of the keywords depends on the location ofoccurrence thereof within the text of the electronic document. It willbe appreciated that the electronic document can comprise varioussections such as introduction, abstract, detailed explanation and soforth. Furthermore, each section of the electronic document will have adifferent significance as compared to the other sections of theelectronic document. For example, the abstract can have a highersignificance as compared to the introduction section. In such aninstance, a keyword occurring at positions within sections having moresignificance will have the preference score more than a keywordoccurring at positions within sections having less significance. In anexample, keywords “leukaemia” and “blood” occur in the abstract sectionof a research document, whereas a word “infection” occurs within theintroduction section of the research document. Furthermore, a relevanceor significance of the abstract section of the research document is morethan the introduction section of the research document. Therefore, thekeywords “leukaemia” and “blood” are assigned a higher preference scoreas compared to the keyword “infection”. Furthermore, the preferencescore of each keyword of the generated set of keywords of the electronicdocument depends on the publicly-available information corresponding tothe keyword. In such an instance, a keyword that has morepublicly-available information associated therewith (for example, theinformation corresponding to the keyword is well-known on internet) willbe assigned higher preference score as compared to a keyword that doesnot have (or that has less) publicly-available information associatedtherewith. In an example, a generated set of keywords comprises thewords “meningitis”, “brain-fever”, “symptoms” and so forth. It will beappreciated that the word “brain-fever” is a synonym of the word“meningitis”. Moreover, the word “meningitis” is a more well-known wordto the public as compared to the word “brain-fever” and therefore, thepublicly-available information associated with the word “meningitis”will be more than the publicly-available information associated with theword “brain-fever”. Therefore, the word “meningitis” will be given ahigher preference score as compared to the word “brain-fever”.

Furthermore, the server arrangement is operable to identify a semanticrelationship of each keyword with other keywords of the generated set ofkeywords. The term “semantic relationship” as used throughout thepresent disclosure, relates to an association between one or morekeywords of the generated set of keywords, wherein the association canbe a technical association, a logical association, a scientificassociation and so forth. In an example, a keyword “drug” can beassociated with a second keyword “disease” in several ways. In such anexample, the “drug” can be known to be effective in treating the“disease”, the “drug” can be known to be ineffective in treating the“disease”, the “drug” can aggravate growth of the “disease” and soforth. Consequently, the associations between the “drug” and the“disease” such as “effective in treating”, “ineffective in treating” or“aggravate growth” can be the semantic relationships between the twokeywords. Optionally, parsing techniques can be implemented to identifythe semantic relationships between keywords of the generated set ofkeywords. The term “parsing techniques” as used throughout the presentdisclosure, relates to an analysis performed by the server arrangement,wherein the server arrangement analyses the semantic relationshipbetween the keywords and generates a result in a hierarchical structure(such as a hierarchical tree). It will be appreciated that thehierarchical structure determines the publicly-available semanticrelationships present between the keywords of the generated set ofkeywords. Optionally, the parsing technique used to identify thesemantic relationships between the generated set of keywords can be aframe semantic parsing technique. Furthermore, each keyword of thegenerated set of keywords can have one or more semantic relationshipswith each other and/or semantic relationships with one or more otherkeywords of the generated set of keywords.

In an embodiment, the server arrangement is operable to identify thesemantic relationship of each keyword with other keywords of thegenerated set of keywords, based on at least one of: significance of thetext of the electronic document and publicly-available informationcorresponding to each keyword of the generated set of keywords of theelectronic document. The semantic relationship between the keywords canbe identified by the significance of the text of the electronicdocument, wherein the significance of the text refers to importance ofthe information (such as, based on a public-necessity for theinformation) provided within the text of the electronic document.Optionally, such a significance of the text can be provided as an inputby the author of the electronic document. In an example, a researchdocument establishes a relationship between flu and the symptomsassociated with the flu. Furthermore, the author concludes in theresearch document that the symptoms associated with the flu are bodypain, nasal congestion, cough and so forth. However, such informationthat concludes that the symptoms associated with the flu are body pain,nasal congestion, cough and so forth, is widely publicly-available andis easily accessible to anyone. Thus, the author provides the input forthe significance of the electronic document as low. In such an example,the semantic relationship between the keywords “flu” and the “symptoms”associated with the flu will be identified based on the significance ofthe text of the electronic document. Alternatively, a research documentestablishes a relationship between flu and previously-unknown geneticfactors that can result in faster recovery from the flu. In such anexample, the significance of the text of the research document will behigh and the semantic relationship between the keyword “flu” and thegenetic factors will be identified based on the significance of the textof the electronic document. Furthermore, the semantic relationshipbetween the keywords can be identified using the publicly-availableinformation corresponding to each keyword of the generated set ofkeywords of the electronic document. The publicly-available association(such as the technical association, the logical association and soforth) of the two or more keywords will be identified by the serverarrangement. In an example, a research document is associated with a“treatment” of “Typhoid”. The research document comprises keywords suchas “Typhoid”, “ceftriaxone”, “ciprofloxacin”, “Tylenol” and so forth. Itwill be appreciated that the publicly-available information about drugs“ceftriaxone” and “ciprofloxacin” concludes that the drugs “ceftriaxone”and “ciprofloxacin” are effective in treating Typhoid, whereas the drug“Tylenol” is ineffective in treating Typhoid. Therefore, the semanticrelationship between the words “ceftriaxone”, “ciprofloxacin” and“Typhoid” will be identified as “effective in treatment” and between thewords “Tylenol” and “Typhoid” will be identified as “ineffective intreatment”.

Furthermore, the server arrangement is operable to determine animportance score of each keyword of the generated set of keywords, basedon the semantic relationship. The server arrangement is operable todetermine the importance score of each keyword, based on the sematicrelationships identified between the keyword and other keywords of thegenerated set of keywords. In an example, when the significance of theelectronic document is low and the semantic relationship is determinedbetween keywords “flu” and “symptoms” within the electronic document, alow importance score is determined for each of “flu” and “symptoms”based on the low significance of the electronic document. It will beappreciated that the server arrangement is operable to assign thepreference score as well as the importance score to each keyword of thegenerated set of keywords.

Furthermore, the server arrangement is operable to determine acumulative rank for each keyword of the generated set of keywords, basedon the determined preference score and the determined importance scoreof the keyword. As mentioned hereinabove, the server arrangement isoperable to determine the preference score and the importance score foreach keyword of the generated set of keywords. Subsequently, the serverarrangement is operable to determine the cumulative rank for eachkeyword using the preference score and the importance score thereof. Theserver arrangement is operable to rank each keyword of the generated setof keywords based on the cumulative ranking assigned to the keyword. Itwill be appreciated that the preference score is determined for anindividual keyword, whereas the importance score is determined for twoor more keywords. In an example, a keyword “cough” appears nine times ina research document. Moreover, a keyword “flu” appears five times in aresearch document. The preference score associated with the word “cough”based on the frequency of occurrence in the research document will bemore than the preference score associated with the word “flu”. Moreover,the semantic relationship between the words “cough” and “flu” isdetermined based on publically-available information that “cough” is asymptom of “flu”. Furthermore, based on the semantic relationshipdetermined between “cough” and “flu”, a same importance score will beassigned to both the words “cough” and “flu”. Thus, the cumulativeranking of the keywords will depend on the individual preference scoresof the keywords, as well as the importance score associated with boththe keywords.

Furthermore, the server arrangement is operable to determine, based onthe cumulative rank of each keyword of the generated set of keywords,the contextually-relevant keywords related to the text of electronicdocument. The server arrangement is operable to assign the cumulativerank to each keyword of the generated set of keywords and thereafter thecontextually-relevant keywords related to the text of electronicdocument are determined. The contextually-relevant keywords relate tothe keywords that are ranked high as compared to other keywords of thegenerated set of keywords. Such contextually-relevant keywordscorrespond to the keywords that can arouse an interest in third-partiestowards the text of the electronic document, without revealingsignificant content published with the electronic document to thethird-parties. Optionally, the keywords that have the low cumulativerank are not accepted as the contextually-relevant keywords and cantherefore be rejected. In an example, a generated set of keywordscomprises 50 keywords. In such an example, the server arrangementgenerates 10 contextually-relevant keywords out of the generated set ofkeywords comprising 50 keywords. It will be appreciated that the 10generated keywords will have high cumulative ranks and therefore, highcontextual-relevance to the text of the electronic document as comparedto the other keywords of the generated set of keywords.

In an embodiment, the server arrangement is further operable to:generate, based on the determined cumulative rank of each keyword of thegenerated set of keywords, a list comprising the determinedcontextually-relevant keywords arranged in at least one of: anincreasing order, or a decreasing order of the cumulative ranks. Thecontextually-relevant keywords determined can be arranged in theincreasing order, or the decreasing order of the cumulative ranksassigned thereto. In an example, the contextually-relevant keywordscomprise the keywords “lung”, “cancer”, “blood” and “bones”.Furthermore, the cumulative rank of the keyword “cancer” is highest, thecumulative ranking of the keyword “lung” is second highest, thecumulative rank of the keyword “blood” is third highest and thecumulative ranking of the keyword “bones” is lowest. Therefore, the listof the determined contextually-relevant keywords can be in an order:cancer, lung, blood and bones, such as, in the decreasing order of thecumulative ranks of the keywords. Furthermore, the list of thedetermined contextually-relevant keywords can be in the order: bones,blood, lung and cancer or in the increasing order of the cumulativeranks of the keywords. Furthermore, the server arrangement is furtheroperable to recommend the list of the contextually-relevant keywords tothe user associated with the electronic document. The server arrangementcan recommend the list of the contextually-relevant keywords to the userof the electronic document, wherein the user can use thecontextually-relevant keywords for several purposes. In an example, theuser is an author of a research document and can use the recommendedlist of the contextually-relevant keywords for generating a summarycorresponding to the research document. It will be appreciated that theusage of the recommended list of the contextually-relevant keywords inthe summary will enable the research document to be more accessible onthe internet, such as, when the summary associated with the researchdocument is shared by the author on the internet (such as for providingan insight of text of the research document that can be purchased fromthe author). In another example, the recommended list of thecontextually-relevant keywords can be used to input thecontextually-relevant keywords in a keyword-field of the researchdocument.

In an embodiment, the server arrangement is operable to implement amachine-learning algorithm for determining the contextually-relevantkeywords related to the text of electronic document. Furthermore, theserver arrangement uses the machine-learning algorithm for performingthe abovementioned steps, such as determining the semantic relationshipsbetween the two or more keywords, determining the importance scorebetween the two or more keywords based on the semantic relationships andso forth. Such a machine-learning algorithm is operable to employ anycomputationally intelligent technique and consequently, adapt to unknownor changing variables for better performance. For example, themachine-learning algorithm is operable to adapt weights assigned to thepreference score and/or the importance score for determining thecumulative rank of each keyword of the generated set of keywords.

In an embodiment, the memory is implemented within a client device andthe data processing arrangement is implemented in one of: the clientdevice, or the server arrangement. The memory that stores the electronicdocument can be implemented in the client device (such as a personalcomputing device of a user), wherein the client device can be a laptopcomputer, a tablet computer, a smartphone, a personal digital assistant(PDA) and so forth. Furthermore, the data processing arrangement can beimplemented on the client device or alternatively, on the serverarrangement.

The present disclosure also relates to the method as described above.Various embodiments and variants disclosed above apply mutatis mutandisto the method.

The method of determining contextually-relevant keywords related to textof an electronic document, comprises extracting the text of theelectronic document; receiving a plurality of words from an ontology;comparing each of the plurality of words with the text of the electronicdocument, to identify common words from the ontology that appear withinthe electronic document; receiving a plurality of synonymous wordscorresponding to the common words; generating a set of keywordscomprising the common words and the plurality of synonymous wordscorresponding to the common words; determining a preference score foreach keyword of the set of keywords, wherein the preference score isbased on one or more preference parameters; identifying a semanticrelationship of each keyword with other keywords of the set of keywords;determining an importance score of each keyword of the set of keywords,based on the semantic relationship; determining a cumulative rank foreach keyword of the set of keywords, based on the preference score andthe importance score of the keyword and determining, based on thecumulative rank of each keyword of the set of keywords, thecontextually-relevant keywords related to the text of electronicdocument.

In an embodiment, the method further comprises storing the electronicdocument in an encrypted format.

In an embodiment, the ontology is a hierarchical ontology comprising aplurality of nodes, and wherein each of the plurality of nodes isassociated with a plurality of words related to a specificsubject-matter.

In an embodiment, the method further comprises:

-   receiving an input from a user associated with the electronic    document, wherein the input corresponds to a subject-matter    associated with the text of the electronic document;-   comparing the subject-matter corresponding to the input with the    subject-matter corresponding to the plurality of nodes;-   identifying, based on the comparison, the node of the plurality of    nodes related to the subject-matter corresponding to the input; and-   receiving the plurality of words associated with the node of the    plurality of nodes.

In an embodiment, the method comprises identifying the common words andreceiving the plurality of synonymous words corresponding to the commonwords at in a:

-   sequential manner, or-   simultaneous manner.

In an embodiment, the one or more preference parameters comprise:frequency of occurrence of each keyword of the set of keywords withinsearch strings previously used by a plurality of users of a searchengine, frequency of occurrence of the common words within the text ofthe electronic document, location of occurrence of the common wordswithin the text of the electronic document and publicly-availableinformation corresponding to each keyword of the generated set ofkeywords of the electronic document.

In an embodiment, the method comprises identifying the semanticrelationship of each keyword with other keywords of the generated set ofkeywords, based on at least one of: significance of the text of theelectronic document and publicly-available information corresponding toeach keyword of the generated set of keywords of the electronicdocument.

In an embodiment, the method further comprises:

-   generating, based on the cumulative rank of each keyword of the set    of keywords, a list comprising the contextually-relevant keywords    arranged in at least one of: an increasing order, or a decreasing    order of the cumulative ranks; and-   recommending the list of the contextually-relevant keywords to the    user associated with the electronic document.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to FIG. 1, there is shown a schematic illustration of a system100 for determining contextually-relevant keywords related to text of anelectronic document, in accordance with an embodiment of the presentdisclosure. The system 100 comprises a memory 102 that is operable tostore the electronic document therein. The system 100 further comprisesa database arrangement 104 comprising an ontology 106 and a synonymdatabank 108. Furthermore, the system 100 comprises a data processingarrangement 110 and a server arrangement 112. As shown, the dataprocessing arrangement is 110 communicatively coupled to the memory 102and the database arrangement 104. Furthermore, the server arrangement112 is communicatively coupled to the database arrangement 104 and thedata processing arrangement 110.

Referring to FIGS. 2A and 2B, there are shown steps of a method 200 fordetermining contextually-relevant keywords related to text of anelectronic document, in accordance with an embodiment of the presentdisclosure. At a step 202, the text of the electronic document isextracted. At a step 204, a plurality of words is received from anontology. At a step 206, each of the received plurality of words iscompared with the text of the electronic document, to identify commonwords from the ontology that appear within the electronic document. At astep 208, a plurality of synonymous words corresponding to theidentified common words is received. At a step 210, a set of keywordscomprising the identified common words and the plurality of synonymouswords corresponding to the identified common words is generated. At astep 212, a preference score for each keyword of the generated set ofkeywords is determined wherein the preference score is based on one ormore preference parameters. At a step 214, a semantic relationship ofeach keyword with other keywords of the generated set of keywords isidentified. At a step 216, an importance score of each keyword of thegenerated set of keywords is determined based on the identified semanticrelationship. At a step 218, a cumulative rank for each keyword of thegenerated set of keywords is determined based on the determinedpreference score and the determined importance score of the keyword. Ata step 220, the contextually-relevant keywords related to the text ofelectronic document are determined based on the cumulative rank of eachkeyword of the generated set of keywords.

The steps 202 to 220 are only illustrative and other alternatives canalso be provided where one or more steps are added, one or more stepsare removed, or one or more steps are provided in a different sequencewithout departing from the scope of the claims herein.

Modifications to embodiments of the present disclosure described in theforegoing are possible without departing from the scope of the presentdisclosure as defined by the accompanying claims. Expressions such as“including”, “comprising”, “incorporating”, “have”, “is” used todescribe and claim the present disclosure are intended to be construedin a non-exclusive manner, namely allowing for items, components orelements not explicitly described also to be present. Reference to thesingular is also to be construed to relate to the plural.

What is claimed is:
 1. A system for determining contextually-relevantkeywords related to text of an electronic document, the systemcomprising: a memory operable to store the electronic document therein;a database arrangement comprising an ontology and a synonym databank; adata processing arrangement communicatively coupled to the memory andthe database arrangement, wherein the data processing arrangement isoperable to fetch the electronic document stored within the memory,extract the text of the electronic document, receive a plurality ofwords from the ontology, compare each of the plurality of words with thetext of the electronic document, to identify common words from theontology that appear within the electronic document, receive from thesynonym databank, a plurality of synonymous words corresponding to thecommon words, and generate a set of keywords comprising the common wordsand the plurality of synonymous words corresponding to the common words;and a server arrangement communicatively coupled to the databasearrangement and the data processing arrangement, wherein the serverarrangement is operable to receive the set of keywords from the dataprocessing arrangement, determine a preference score for each keyword ofthe set of keywords, wherein the preference score is based on one ormore preference parameters, identify a semantic relationship of eachkeyword with other keywords of the set of keywords, determine animportance score of each keyword of the set of keywords, based on thesemantic relationship, determine a cumulative rank for each keyword ofthe set of keywords, based on the preference score and the importancescore of the keyword, and determine, based on the cumulative rank ofeach keyword of the set of keywords, the contextually-relevant keywordsrelated to the text of electronic document.
 2. The system of claim 1,wherein the electronic document is stored within the memory in anencrypted format.
 3. The system of any one of the claims 1, wherein thememory is implemented within a client device and wherein the dataprocessing arrangement is implemented in one of: the client device, orthe server arrangement.
 4. The system of any one of the claims 1,wherein the ontology is a hierarchical ontology comprising a pluralityof nodes, and wherein each of the plurality of nodes is associated witha plurality of words related to a specific subject-matter.
 5. The systemof claim 4, wherein the data processing arrangement is further operableto: receive an input from a user associated with the electronicdocument, wherein the received input corresponds to a subject-matterassociated with the text of the electronic document; compare thesubject-matter corresponding to the input with the subject-mattercorresponding to the plurality of nodes; identify, based on thecomparison, the node of the plurality of nodes related to thesubject-matter corresponding to the input; and receive the plurality ofwords associated with the node of the plurality of nodes.
 6. The systemof claim 1, wherein the data processing arrangement is operable toidentify the common words and receive the plurality of synonymous wordscorresponding to the identified common words in a: sequential manner, orsimultaneous manner.
 7. The system of claim 1, wherein the one or morepreference parameters comprise: frequency of occurrence of each keywordof the set of keywords within search strings previously used by aplurality of users of a search engine, frequency of occurrence of thecommon words within the text of the electronic document, location ofoccurrence of the common words within the text of the electronicdocument and publicly-available information corresponding to eachkeyword of the generated set of keywords of the electronic document. 8.The system of claim 1, wherein the server arrangement is operable toidentify the semantic relationship of each keyword with other keywordsof the generated set of keywords, based on at least one of: significanceof the text of the electronic document and publicly-availableinformation corresponding to each keyword of the generated set ofkeywords of the electronic document.
 9. The system of claim 5, whereinthe server arrangement is further operable to: generate, based on thecumulative rank of each keyword of the set of keywords, a listcomprising the contextually-relevant keywords arranged in at least oneof: an increasing order, or a decreasing order of the cumulative ranks;and recommend the list of the contextually-relevant keywords to the userassociated with the electronic document.
 10. The system of claim 1,wherein the server arrangement is operable to implement amachine-learning algorithm for determining the contextually-relevantkeywords related to the text of electronic document.
 11. A method fordetermining contextually-relevant keywords related to text of anelectronic document, wherein the method comprises: extracting the textof the electronic document; receiving a plurality of words from anontology; comparing each of the plurality of words with the text of theelectronic document, to identify common words from the ontology thatappear within the electronic document; receiving a plurality ofsynonymous words corresponding to the common words; generating a set ofkeywords comprising the common words and the plurality of synonymouswords corresponding to the common words; determining a preference scorefor each keyword of the set of keywords, wherein the preference score isbased on one or more preference parameters; identifying a semanticrelationship of each keyword with other keywords of the set of keywords;determining an importance score of each keyword of the set of keywords,based on the semantic relationship; determining a cumulative rank foreach keyword of the set of keywords, based on the preference score andthe importance score of the keyword; and determining, based on thecumulative rank of each keyword of the set of keywords, thecontextually-relevant keywords related to the text of electronicdocument.
 12. The method of claim 11, wherein the method furthercomprises storing the electronic document in an encrypted format. 13.The method of any one of the claims 11, wherein the ontology is ahierarchical ontology comprising a plurality of nodes, and wherein eachof the plurality of nodes is associated with a plurality of wordsrelated to a specific subject-matter.
 14. The method of claim 13,wherein the method further comprises: receiving an input from a userassociated with the electronic document, wherein the input correspondsto a subject-matter associated with the text of the electronic document;comparing the subject-matter corresponding to the input with thesubject-matter corresponding to the plurality of nodes; identifying,based on the comparison, the node of the plurality of nodes related tothe subject-matter corresponding to the input; and receiving theplurality of words associated with the node of the plurality of nodes.15. The method of claim 11, wherein the method comprises identifying thecommon words and receiving the plurality of synonymous wordscorresponding to the common words at in a: sequential manner, orsimultaneous manner.
 16. The method of claim 11, wherein the one or morepreference parameters comprise: frequency of occurrence of each keywordof the set of keywords within search strings previously used by aplurality of users of a search engine, frequency of occurrence of thecommon words within the text of the electronic document, location ofoccurrence of the common words within the text of the electronicdocument and publicly-available information corresponding to eachkeyword of the generated set of keywords of the electronic document. 17.The method of claim 11, wherein the method comprises identify thesemantic relationship of each keyword with other keywords of thegenerated set of keywords, based on at least one of: significance of thetext of the electronic document and publicly-available informationcorresponding to each keyword of the generated set of keywords of theelectronic document.
 18. The method claim 14, wherein the method furthercomprises: generating, based on the cumulative rank of each keyword ofthe set of keywords, a list comprising the contextually-relevantkeywords arranged in at least one of: an increasing order, or adecreasing order of the cumulative ranks; and recommending the list ofthe contextually-relevant keywords to the user associated with theelectronic document.
 19. A software product recorded on machine-readablenon-transient data storage media, wherein the software product isexecutable upon computing hardware to implement a method of claim 11.